Overview

Dataset statistics

Number of variables12
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory107.4 B

Variable types

Categorical5
Numeric6
DateTime1

Dataset

Description제공범위(대상) : 지방세 부과액에 대한 세목별 징수현황을 제공 관련법령 : 지방세징수법 소관기관 : 지방자치단체 제공기관 : 시군구 표준데이터셋 제공시스템 : 표준지방세시스템 자료기준일 : 매년 12월31일
URLhttps://www.data.go.kr/data/15079025/fileData.do

Alerts

시도명 has constant value ""Constant
과세년도 has constant value ""Constant
데이터기준일 has constant value ""Constant
자치단체코드 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 자치단체코드High correlation
부과금액 is highly overall correlated with 수납급액 and 4 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 has 17 (43.6%) zerosZeros
수납급액 has 17 (43.6%) zerosZeros
환급금액 has 19 (48.7%) zerosZeros
결손금액 has 23 (59.0%) zerosZeros
미수납 금액 has 21 (53.8%) zerosZeros
징수율 has 17 (43.6%) zerosZeros

Reproduction

Analysis started2023-12-12 12:36:45.884108
Analysis finished2023-12-12 12:36:50.070196
Duration4.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
경상북도
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도
2nd row경상북도
3rd row경상북도
4th row경상북도
5th row경상북도

Common Values

ValueCountFrequency (%)
경상북도 39
100.0%

Length

2023-12-12T21:36:50.132094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:36:50.239500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 39
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
포항시
13 
포항시남구
13 
포항시북구
13 

Length

Max length5
Median length5
Mean length4.3333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포항시
2nd row포항시
3rd row포항시
4th row포항시
5th row포항시

Common Values

ValueCountFrequency (%)
포항시 13
33.3%
포항시남구 13
33.3%
포항시북구 13
33.3%

Length

2023-12-12T21:36:50.345339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:36:50.482843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포항시 13
33.3%
포항시남구 13
33.3%
포항시북구 13
33.3%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
47110
13 
47111
13 
47113
13 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47110
2nd row47110
3rd row47110
4th row47110
5th row47110

Common Values

ValueCountFrequency (%)
47110 13
33.3%
47111 13
33.3%
47113 13
33.3%

Length

2023-12-12T21:36:50.585901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:36:50.698998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47110 13
33.3%
47111 13
33.3%
47113 13
33.3%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
2021
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 39
100.0%

Length

2023-12-12T21:36:50.817599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:36:50.941647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 39
100.0%

세목명
Categorical

Distinct13
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size444.0 B
레저세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (8)
24 

Length

Max length7
Median length5
Mean length4.4615385
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row레저세
2nd row재산세
3rd row주민세
4th row취득세
5th row자동차세

Common Values

ValueCountFrequency (%)
레저세 3
 
7.7%
재산세 3
 
7.7%
주민세 3
 
7.7%
취득세 3
 
7.7%
자동차세 3
 
7.7%
과년도수입 3
 
7.7%
담배소비세 3
 
7.7%
도시계획세 3
 
7.7%
등록면허세 3
 
7.7%
지방교육세 3
 
7.7%
Other values (3) 9
23.1%

Length

2023-12-12T21:36:51.065953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 3
 
7.7%
재산세 3
 
7.7%
주민세 3
 
7.7%
취득세 3
 
7.7%
자동차세 3
 
7.7%
과년도수입 3
 
7.7%
담배소비세 3
 
7.7%
도시계획세 3
 
7.7%
등록면허세 3
 
7.7%
지방교육세 3
 
7.7%
Other values (3) 9
23.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8311411 × 1010
Minimum0
Maximum1.17495 × 1011
Zeros17
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T21:36:51.207118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.104529 × 109
Q32.3378568 × 1010
95-th percentile8.343119 × 1010
Maximum1.17495 × 1011
Range1.17495 × 1011
Interquartile range (IQR)2.3378568 × 1010

Descriptive statistics

Standard deviation2.7877693 × 1010
Coefficient of variation (CV)1.5224219
Kurtosis4.0213007
Mean1.8311411 × 1010
Median Absolute Deviation (MAD)6.104529 × 109
Skewness2.0241953
Sum7.1414502 × 1011
Variance7.7716578 × 1020
MonotonicityNot monotonic
2023-12-12T21:36:51.340788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 17
43.6%
58975527000 1
 
2.6%
5832426000 1
 
2.6%
44098471000 1
 
2.6%
23217576000 1
 
2.6%
7310809000 1
 
2.6%
14000047000 1
 
2.6%
27672607000 1
 
2.6%
117495000000 1
 
2.6%
4083250000 1
 
2.6%
Other values (13) 13
33.3%
ValueCountFrequency (%)
0 17
43.6%
4083250000 1
 
2.6%
5832426000 1
 
2.6%
6104529000 1
 
2.6%
7038838000 1
 
2.6%
7310809000 1
 
2.6%
7817586000 1
 
2.6%
14000047000 1
 
2.6%
14501523000 1
 
2.6%
16359890000 1
 
2.6%
ValueCountFrequency (%)
117495000000 1
2.6%
86672779000 1
2.6%
83071014000 1
2.6%
58975527000 1
2.6%
45158765000 1
2.6%
44412136000 1
2.6%
44098471000 1
2.6%
37190023000 1
2.6%
27672607000 1
2.6%
23539560000 1
2.6%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7427658 × 1010
Minimum-2.964939 × 109
Maximum1.17161 × 1011
Zeros17
Zeros (%)43.6%
Negative1
Negative (%)2.6%
Memory size483.0 B
2023-12-12T21:36:51.473658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.964939 × 109
5-th percentile0
Q10
median4.501134 × 109
Q32.173424 × 1010
95-th percentile8.2968086 × 1010
Maximum1.17161 × 1011
Range1.2012594 × 1011
Interquartile range (IQR)2.173424 × 1010

Descriptive statistics

Standard deviation2.7796917 × 1010
Coefficient of variation (CV)1.5949886
Kurtosis4.192921
Mean1.7427658 × 1010
Median Absolute Deviation (MAD)4.501134 × 109
Skewness2.0675101
Sum6.7967867 × 1011
Variance7.7266858 × 1020
MonotonicityNot monotonic
2023-12-12T21:36:51.602338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 17
43.6%
58975527000 1
 
2.6%
5718487000 1
 
2.6%
41941921000 1
 
2.6%
22227898000 1
 
2.6%
7292140000 1
 
2.6%
4501134000 1
 
2.6%
24946665000 1
 
2.6%
117161000000 1
 
2.6%
3986971000 1
 
2.6%
Other values (13) 13
33.3%
ValueCountFrequency (%)
-2964939000 1
 
2.6%
0 17
43.6%
3986971000 1
 
2.6%
4501134000 1
 
2.6%
5718487000 1
 
2.6%
6088007000 1
 
2.6%
7292140000 1
 
2.6%
7586229000 1
 
2.6%
14501523000 1
 
2.6%
16359890000 1
 
2.6%
ValueCountFrequency (%)
117161000000 1
2.6%
84956686000 1
2.6%
82747130000 1
2.6%
58975527000 1
2.6%
43847017000 1
2.6%
43196068000 1
2.6%
41941921000 1
2.6%
37190023000 1
2.6%
24946665000 1
2.6%
22227898000 1
2.6%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3882659 × 108
Minimum0
Maximum9.026884 × 109
Zeros19
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T21:36:51.748235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median348000
Q31.05107 × 108
95-th percentile2.7711682 × 109
Maximum9.026884 × 109
Range9.026884 × 109
Interquartile range (IQR)1.05107 × 108

Descriptive statistics

Standard deviation1.6129373 × 109
Coefficient of variation (CV)2.9934256
Kurtosis21.024625
Mean5.3882659 × 108
Median Absolute Deviation (MAD)348000
Skewness4.3313698
Sum2.1014237 × 1010
Variance2.6015667 × 1018
MonotonicityNot monotonic
2023-12-12T21:36:51.878383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 19
48.7%
791000 1
 
2.6%
14415000 1
 
2.6%
1683989000 1
 
2.6%
163377000 1
 
2.6%
29680000 1
 
2.6%
2657197000 1
 
2.6%
390805000 1
 
2.6%
625514000 1
 
2.6%
32877000 1
 
2.6%
Other values (11) 11
28.2%
ValueCountFrequency (%)
0 19
48.7%
348000 1
 
2.6%
791000 1
 
2.6%
4555000 1
 
2.6%
14415000 1
 
2.6%
15774000 1
 
2.6%
21601000 1
 
2.6%
29110000 1
 
2.6%
29680000 1
 
2.6%
32877000 1
 
2.6%
ValueCountFrequency (%)
9026884000 1
2.6%
3796909000 1
2.6%
2657197000 1
2.6%
1909011000 1
2.6%
1683989000 1
2.6%
625514000 1
2.6%
390805000 1
2.6%
330098000 1
2.6%
234465000 1
2.6%
163377000 1
2.6%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90247641
Minimum0
Maximum2.196253 × 109
Zeros23
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T21:36:52.029683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3260000
95-th percentile2.64052 × 108
Maximum2.196253 × 109
Range2.196253 × 109
Interquartile range (IQR)260000

Descriptive statistics

Standard deviation3.9208984 × 108
Coefficient of variation (CV)4.3445993
Kurtosis24.010167
Mean90247641
Median Absolute Deviation (MAD)0
Skewness4.8246445
Sum3.519658 × 109
Variance1.5373444 × 1017
MonotonicityNot monotonic
2023-12-12T21:36:52.188075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 23
59.0%
993000 1
 
2.6%
12000 1
 
2.6%
166302000 1
 
2.6%
329000 1
 
2.6%
200000 1
 
2.6%
2196253000 1
 
2.6%
356000 1
 
2.6%
158000 1
 
2.6%
64000 1
 
2.6%
Other values (7) 7
 
17.9%
ValueCountFrequency (%)
0 23
59.0%
12000 1
 
2.6%
30000 1
 
2.6%
64000 1
 
2.6%
158000 1
 
2.6%
188000 1
 
2.6%
200000 1
 
2.6%
320000 1
 
2.6%
329000 1
 
2.6%
356000 1
 
2.6%
ValueCountFrequency (%)
2196253000 1
2.6%
1143802000 1
2.6%
166302000 1
2.6%
8938000 1
2.6%
1212000 1
2.6%
993000 1
2.6%
501000 1
2.6%
356000 1
2.6%
329000 1
2.6%
320000 1
2.6%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9352828 × 108
Minimum0
Maximum8.859975 × 109
Zeros21
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T21:36:52.330832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36.48957 × 108
95-th percentile3.1832934 × 109
Maximum8.859975 × 109
Range8.859975 × 109
Interquartile range (IQR)6.48957 × 108

Descriptive statistics

Standard deviation1.8771179 × 109
Coefficient of variation (CV)2.3655337
Kurtosis11.808298
Mean7.9352828 × 108
Median Absolute Deviation (MAD)0
Skewness3.371723
Sum3.0947603 × 1010
Variance3.5235716 × 1018
MonotonicityNot monotonic
2023-12-12T21:36:52.465010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 21
53.8%
1310755000 1
 
2.6%
113927000 1
 
2.6%
1990248000 1
 
2.6%
989349000 1
 
2.6%
18469000 1
 
2.6%
7302660000 1
 
2.6%
2725586000 1
 
2.6%
334750000 1
 
2.6%
96215000 1
 
2.6%
Other values (9) 9
23.1%
ValueCountFrequency (%)
0 21
53.8%
16334000 1
 
2.6%
18469000 1
 
2.6%
96215000 1
 
2.6%
113927000 1
 
2.6%
119633000 1
 
2.6%
231357000 1
 
2.6%
323884000 1
 
2.6%
334750000 1
 
2.6%
963164000 1
 
2.6%
ValueCountFrequency (%)
8859975000 1
2.6%
7302660000 1
2.6%
2725586000 1
2.6%
2629286000 1
2.6%
1990248000 1
2.6%
1707155000 1
2.6%
1310755000 1
2.6%
1214856000 1
2.6%
989349000 1
2.6%
963164000 1
2.6%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.690256
Minimum-42.12
Maximum100
Zeros17
Zeros (%)43.6%
Negative1
Negative (%)2.6%
Memory size483.0 B
2023-12-12T21:36:52.598995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-42.12
5-th percentile0
Q10
median88.83
Q398.035
95-th percentile100
Maximum100
Range142.12
Interquartile range (IQR)98.035

Descriptive statistics

Standard deviation50.372394
Coefficient of variation (CV)1.0137278
Kurtosis-1.9077983
Mean49.690256
Median Absolute Deviation (MAD)11.17
Skewness-0.14472798
Sum1937.92
Variance2537.3781
MonotonicityNot monotonic
2023-12-12T21:36:52.763471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 17
43.6%
100.0 4
 
10.3%
97.26 1
 
2.6%
98.05 1
 
2.6%
95.11 1
 
2.6%
95.74 1
 
2.6%
99.74 1
 
2.6%
32.15 1
 
2.6%
90.15 1
 
2.6%
99.71 1
 
2.6%
Other values (10) 10
25.6%
ValueCountFrequency (%)
-42.12 1
 
2.6%
0.0 17
43.6%
32.15 1
 
2.6%
88.83 1
 
2.6%
90.15 1
 
2.6%
94.72 1
 
2.6%
95.11 1
 
2.6%
95.74 1
 
2.6%
97.04 1
 
2.6%
97.1 1
 
2.6%
ValueCountFrequency (%)
100.0 4
10.3%
99.74 1
 
2.6%
99.73 1
 
2.6%
99.71 1
 
2.6%
99.61 1
 
2.6%
99.44 1
 
2.6%
98.05 1
 
2.6%
98.02 1
 
2.6%
97.64 1
 
2.6%
97.26 1
 
2.6%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
Minimum2021-12-31 00:00:00
Maximum2021-12-31 00:00:00
2023-12-12T21:36:52.878911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:52.975270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T21:36:49.146403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:46.321605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:46.854996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.337725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.768908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:48.258972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:49.255899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:46.412745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:46.943937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.414508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.844049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:48.658191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:49.349145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:46.500589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.030729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.488369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.926745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:48.752234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:49.452790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:46.580895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.100520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.560082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.994243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:48.842856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:49.574469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:46.668772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.175501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.626474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:48.064655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:48.934445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:49.663022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:46.763614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.249525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:47.690663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:48.135670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:36:49.029709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:36:53.042085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명부과금액수납급액환급금액결손금액미수납 금액징수율
시군구명1.0001.0000.0000.0000.1890.0000.0910.3400.198
자치단체코드1.0001.0000.0000.0000.1890.0000.0910.3400.198
세목명0.0000.0001.0000.7150.4910.2290.2660.5140.401
부과금액0.0000.0000.7151.0000.9940.5110.0000.5680.436
수납급액0.1890.1890.4910.9941.0000.5840.0000.4230.514
환급금액0.0000.0000.2290.5110.5841.0000.8180.7980.737
결손금액0.0910.0910.2660.0000.0000.8181.0001.0001.000
미수납 금액0.3400.3400.5140.5680.4230.7981.0001.0000.942
징수율0.1980.1980.4010.4360.5140.7371.0000.9421.000
2023-12-12T21:36:53.155324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치단체코드세목명시군구명
자치단체코드1.0000.0001.000
세목명0.0001.0000.000
시군구명1.0000.0001.000
2023-12-12T21:36:53.252163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율시군구명자치단체코드세목명
부과금액1.0000.9500.7850.6290.7170.7810.0000.0000.382
수납급액0.9501.0000.6520.4980.5810.8480.0770.0770.232
환급금액0.7850.6521.0000.8710.9450.4650.0000.0000.056
결손금액0.6290.4980.8711.0000.9220.2950.0000.0000.095
미수납 금액0.7170.5810.9450.9221.0000.3230.1310.1310.235
징수율0.7810.8480.4650.2950.3231.0000.1800.1800.190
시군구명0.0000.0770.0000.0000.1310.1801.0001.0000.000
자치단체코드0.0000.0770.0000.0000.1310.1801.0001.0000.000
세목명0.3820.2320.0560.0950.2350.1900.0000.0001.000

Missing values

2023-12-12T21:36:49.833371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:36:50.011070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일
0경상북도포항시471102021레저세000000.02021-12-31
1경상북도포항시471102021재산세000000.02021-12-31
2경상북도포항시471102021주민세000000.02021-12-31
3경상북도포항시471102021취득세000000.02021-12-31
4경상북도포항시471102021자동차세5897552700058975527000000100.02021-12-31
5경상북도포항시471102021과년도수입000000.02021-12-31
6경상북도포항시471102021담배소비세371900230003719002300079100000100.02021-12-31
7경상북도포항시471102021도시계획세000000.02021-12-31
8경상북도포항시471102021등록면허세000000.02021-12-31
9경상북도포항시471102021지방교육세163598900001635989000034800000100.02021-12-31
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일
29경상북도포항시북구471132021취득세11749500000011716100000062551400015800033475000099.712021-12-31
30경상북도포항시북구471132021자동차세2767260700024946665000390805000356000272558600090.152021-12-31
31경상북도포항시북구471132021과년도수입14000047000450113400026571970002196253000730266000032.152021-12-31
32경상북도포항시북구471132021담배소비세000000.02021-12-31
33경상북도포항시북구471132021도시계획세000000.02021-12-31
34경상북도포항시북구471132021등록면허세73108090007292140000296800002000001846900099.742021-12-31
35경상북도포항시북구471132021지방교육세232175760002222789800016337700032900098934900095.742021-12-31
36경상북도포항시북구471132021지방소득세44098471000419419210001683989000166302000199024800095.112021-12-31
37경상북도포항시북구471132021지방소비세000000.02021-12-31
38경상북도포항시북구471132021지역자원시설세58324260005718487000144150001200011392700098.052021-12-31